Model governance and explainability are essential in building ethical AI technology which will stand up to audit. The role of modeling in the development process of AI applications when using blockchain should be the foundation for the application. This practice gives the analytic model its own entity, life, structure and description. Model development, when employed as a structured process can provide all the detailed documentation to ensure that all AI elements have gone through the proper review. This process provides the necessary explainability that is essential in eradicating bias from the analytic models used in making AI-driven decisions. The following article take a deep dive into the how-to for designing and building analytical models that use blockchain for AI.
Even though Bitcoin is the most famous instantiation of blockchain technology, we are just beginning to discover the true potential of this system, which records transactions of any kind and maintains the record across a peer-to-peer network.
In 2018, I turned my thoughts on blockchain inward, producing a patent application (16/128,359 USA) around using blockchain to ensure that all of the decisions made about a machine learning (ML) model are being recorded and are auditable.
These include the model’s variables, model design, training and test data utilized, selection of features, the ability to view the model’s raw latent features, and recording to the blockchain all scientists who built different portions of the variable sets, participated in model weight creation and model testing.